# -*- coding: utf-8 -*-
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from datetime import timedelta

from jms.dm.dm_centersend_timerate_detail_test_dt import jms_dm__dm_centersend_timerate_detail_test_dt

jms_dm__dm_centersend_timerate_summary_test_dt = SparkSqlOperator(
    task_id='jms_dm__dm_centersend_timerate_summary_test_dt',
    task_concurrency=1,
    pool_slots=2,
    master='yarn',
    #execution_timeout=timedelta(hours=2)
    #excel平均时长:0
    execution_timeout = timedelta(minutes=15),
    email=['wangmenglei@jtexpress.com','yl_bigdata@yl-scm.com'],
    name='jms_dm__dm_centersend_timerate_summary_test_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dm/dm_centersend_timerate_summary_test_dt/execute.sql',
    driver_memory='4G' ,
    executor_memory='3G' ,
    executor_cores=2 ,
    num_executors=2,
    retries=0,
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors'             : 2 ,
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead'             : '2G' ,
          'spark.sql.shuffle.partitions': 200
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions.pernode': 200,
              'hive.exec.max.dynamic.partitions': 200
              },
)

jms_dm__dm_centersend_timerate_summary_test_dt << [
    jms_dm__dm_centersend_timerate_detail_test_dt
]